Federal and State Tax law has become so complex that it is now estimated that each year Americans alone use over 6 billion person hours, and spend nearly 4 billion dollars, in an effort to comply with Federal and State Tax statutes. Given this level of complexity and cost, it is not surprising that more and more taxpayers find it necessary to obtain help, in one form or another, to prepare their taxes. Tax return preparation systems, such as tax return preparation software programs and applications, represent a potentially flexible, highly accessible, and affordable source of tax preparation assistance. However, traditional tax return preparation systems are, by design, fairly generic in nature and often lack the malleability to meet the particular needs of a given user.
For instance, traditional tax return preparation systems often present a fixed, e.g., predetermined and pre-packaged, structure or sequence of questions to all users as part of the traditional tax return preparation interview process. This is largely due to the fact that the traditional tax return preparation system analytics use a sequence of interview questions, and/or other user experiences, that are static features and that are typically hard-coded elements of the traditional tax return preparation system and do not lend themselves to effective or efficient modification. As a result, the user experience, and any analysis associated with the interview process and user experience, is a largely inflexible component of a given version of the traditional tax return preparation system. Consequently, the interview processes and/or the user experience of traditional tax return preparation systems can only be modified through a redeployment of the tax return preparation system itself. Therefore, there is little or no opportunity for any analytics associated with the interview process, and/or user experience, to evolve to meet a changing situation or the particular needs of a given taxpayer, even as more information about that taxpayer, and their particular circumstances, is obtained.
As an example, when using traditional tax return preparation systems, the sequence of questions and the other user experience elements presented to a user are pre-determined and are based on a generic user model that is, in fact and by design, not accurately representative of any “real world” user. Consequently, irrelevant, and often confusing, interview questions are virtually always presented to any given real world user. It is therefore not surprising that many users, if not all users, of these traditional tax return preparation systems experience, at best, an impersonal, unnecessarily long, confusing, and complicated, interview process and user experience. Clearly, this is not the type of impression that results in happy, loyal, repeat customers.
Although, traditional tax return preparation systems may provide users with an impersonal interview process, resolving such a deficiency is not an easy task. For example, even if a service provider wanted to customize a traditional tax return preparation system flow or interview process for particular users, figuring out what the user wants to see or expects to see from the service provider can be as arduous a task as actually customizing the traditional tax return preparation system. Simply asking users would be an insufficient solution because many users themselves do not want to waste the time to answer questionnaires. Even more challenging to resolve is the dilemma that many of the users themselves likely do not know what their preferences are, until an option is presented to them. Thus, even if service providers of traditional tax return system wanted to customize their system to particular users' situations, such a task is quite daunting.
What is needed is a method and system for adjusting analytics model characteristics to reduce uncertainty in determining users' preferences for user experience options, to support providing personalized user experiences to users, at least partially based on the users' likely preferences, according to various embodiments.